Attachment insecurities, caregiver burden, and psychological distress among partners of patients with heart disease
Bibliographic record
Abstract
Caregiver psychological distress (i.e., depression and anxiety) is harmful to both caregiver and patient. Different affect-regulation strategies associated with attachment orientations may impact a caregiver's perception of their caregiving role as a burden, thereby contributing to their psychological distress. The aim of the present investigation was to examine the links among attachment orientations, caregiver burden, and psychological distress in a cardiac context. Participants (N = 181, Mage = 61.79, SD = 10.49; males = 24.7%) were romantic partners of patients with heart disease (i.e., informal caregivers) who completed validated questionnaires. The majority of caregivers had partners with coronary artery disease (n = 127, 70. 2%). 66.3% of caregivers reported low burden, 87.6% reported low levels of depression and 89.9% reported low levels of anxiety. The mean anxious attachment score was 2.74 (SD = 1.37) and the mean avoidant attachment score was 2.95 (SD = 1.26). Four mediation analyses were run using PROCESS macro for IBM SPSS (version 26). Statistical models showed that the relationships between attachment anxiety and psychological distress were mediated by caregiver burden [abanxiety= 0.15, 95% C.I. (0.04, 0.29); abdepression = 0.15, 95% C.I. (0.05, 0.28)] and that attachment avoidance was not a significant covariate (cvanxiety = -0.02, p>0.05; cvdepression = 0.40, p>0.05). The relationships between attachment avoidance and psychological distress were also mediated by caregiver burden [abanxiety = 0.23, 95% C.I. (0.10, 0.42); abdepression = 0.21, 95% C.I. (0.09, 0.37]with attachment anxiety as a significant covariate (cvanxiety = 1.09, p<0.001; cvdepression = 1.09, p<0.001). Interventions for caregivers reporting attachment insecurity and burden should be explored to potentially lessen caregiver distress as they support their partners with heart disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".